Vehicle object detection

ABSTRACT

An object detection method includes obtaining a first video stream having video images with object detection data of an area, identifying an object in the area by identifying one or more first object detection data of the object in the first video stream corresponding to validated object detection data, determining a position of the object in the area, obtaining a second video stream having video images with object detection data of the area, identifying second object detection data of the second video stream at the position of the object and determining whether the second object detection data is un-validated object detection data, and updating the validated object detection data of the object detection system if it is determined that the second object detection data of the object is un-validated object detection data.

RELATED APPLICATION DATA

This application is a continuation of International Patent ApplicationNo. PCT/CN2020/124145, filed Oct. 27, 2020, which claims the benefit ofEuropean Patent Application No. 19207494.6, filed Nov. 6, 2019, thedisclosures of which are incorporated herein by reference in theirentireties.

TECHNICAL FIELD

The present disclosure relates to the field of detection systems forvehicles. More particularly, it relates to a method, a computer programand a system for object detection training.

BACKGROUND

Object detections performed by vehicles, such as pedestrian detection,vehicle detection, animal detection, obstacle detection etc. aretypically based on some kind of algorithm that has been tuned by meansof real word data in order to give an as good detection performance aspossible.

Typically, these algorithms involve manual analyzation and labelling ofthe real word data, and tuning the algorithms may hence becomecumbersome and high in costs.

U.S. Pat. No. 9,158,971 B2 describes a system and method for enablinggeneration of a specific object detector for a category of interest. Themethod includes identifying seed objects in frames of a video sequencewith a pre-trained generic detector for the category. An appearancemodel is iteratively learned for each of the seed objects using otherframes in which the seed object is identified.

However, in scenarios where a video stream has inferior quality, or theline of sight is partially blocked it may become difficult for thealgorithm to determine that an object comprised in the video stream isactually an object that is to be detected.

There is thus a need for improved methods and systems for objectdetection by a vehicle.

SUMMARY

An object of the present disclosure is to provide a method, a system anda computer program product where the previously mentioned problems areavoided or at least mitigated. This object is at least partly achievedby the features of the independent claims.

It should be emphasized that the term “comprises/comprising”(replaceable by “includes/including”) when used in this specification istaken to specify the presence of stated features, integers, steps, orcomponents, but does not preclude the presence or addition of one ormore other features, integers, steps, components, or groups thereof. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise.

Generally, when an arrangement or system is referred to herein, it is tobe understood as a physical product; e.g., an apparatus. The physicalproduct may comprise one or more parts, such as controlling circuitry inthe form of one or more controllers, one or more processors, or thelike.

A first aspect is a method for training an object detection system for avehicle. The object detection system comprises validated objectdetection data (VODD) of one or more objects. The method comprises thesteps of:

-   -   obtaining a first video stream comprising video images with        object detection data of an area,    -   identifying an object in the area by identifying one or more        first object detection data of the object in the first video        stream corresponding to validated object detection data,    -   determining a position of the object in the area,    -   obtaining a second video stream comprising video images with        object detection data of the area,    -   identifying second object detection data of the second video        stream at the position of the object and determining whether the        second object detection data is un-validated object detection        data,    -   updating the validated object detection data of the object        detection system if it is determined that the second object        detection data of the object is un-validated object detection        data, thereby training the object detection system to recognize        patterns in un-validated object detection data.

An advantage with the above aspect is that training of e.g. a detectionalgorithm of an object detection system for a vehicle becomes morereliable in terms of detecting objects since video streams fromdifferent vehicles featuring the same area are used for object detectiontraining. Hence, if one vehicle has confirmed that it has detected e.g.a pedestrian, then the video streams of other vehicles covering the samearea, but which may not be able to verify the object, may be used toupdate the object detection system in order to train the system tobetter recognize/detect objects.

Another advantage with the above embodiment is that object data which isgathered from the vehicles and which covers the same area and positionin which an object has been validated, i.e. if object data that isvalidated object detection data from one vehicle clearly able toidentify the object at the same area and position exists, but which datafrom another vehicle does not unambiguously show the object, i.e.un-validated object detection data, may preferably be used for updatingthe object detection system. By using object detection data thatcomprise un-validated object detection data (i.e. it is not confirmedthat the data actually portray the object), the system may be trained torecognize the object even if the image data is not complete, or the lineof sight is obstructed. This is based on that the system knows that aconfirmed object is at that position based on the first video streamcomprising video images with validated object detection data. Thus, byupdating the validated object detection data of the object detectionsystem if it is determined that the second object detection data isun-validated object detection data, a better training algorithm can bedeveloped compared to if only validated images of the object were usedfor training since the system may be trained to recognize patterns inun-validated data.

In some embodiments, the step of obtaining the first video streamcomprising video images with object detection data of the area comprisesreceiving the first video stream from a first vehicle.

An advantage with the above embodiments is that live data for an areamay be gathered easily.

In some embodiments, the step of obtaining the second video streamcomprising video images with object detection data of the area comprisesreceiving the second video stream from a second vehicle.

An advantage with the above embodiment is that data for an area may begathered easily. By gathering data from a second vehicle over the samearea, the object detection system may receive diverse data over the samearea, which may be used for updating the system. The diverse dataprovides better training and higher granularity compared to if data wasonly received from one vehicle.

In some embodiments, the method may further comprise storing, at thesecond vehicle, the second video stream comprising video images withun-validated object detection data.

An advantage with the above embodiment is that data storage may bereduced since not all video streams have to be stored, but rather videostreams that may be used as learning material (i.e. comprising videoimages with un-validated object detection data) may be stored locallyand used at a later point in time.

In some embodiments, the method may further comprise determining a timestamp of the first and second video streams and a vehicle location,wherein the time stamp indicating when the respective video stream wasobtained, and wherein the vehicle location indicates a geographicalposition where the first and second video streams were obtained.

An advantage with the above embodiment is that only video streams thathave been obtained/recorded at a valid point in time may be taken intoconsideration. I.e. vehicles that have video streams covering the areaof the object, but which were recorded at a different point of time thana video stream having identified valid object detection data may in someembodiments not be taken into consideration.

Another advantage with the above embodiments is that the geographicallocation and/or orientation of the vehicle recording the video stream isdetermined and taken into account in order to easier determine theposition of the object and in some embodiments to further determinewhich video streams should be taken into account for training thesystem.

In some embodiments, determining whether the second object detectiondata is un-validated object detection data comprises correlating thesecond object detection data to validated object detection data andbased on the correlation determining a confidence value of the secondobject detection data, wherein if the confidence value is determined tobe below a confidence threshold, the second object detection data isdetermined to be un-validated object detection data.

An advantage with the above embodiments is that determining acorrelation, e.g. a confidence value, between validated object detectiondata from the system or the first video stream and object detection dataof the second video stream enables quick determination of whether theobject detection data of the second video stream is valid or un-validobject detection data.

In some embodiments, the validated object detection data that the secondobject detection data is correlated against is the validated objectdetection data of the first video stream.

An advantage of the above embodiment is that correlation is made betweenvalidated data covering the same area, and hence probably the sameobject is to be verified. The update of the object detection system maythus be made based on the content of the first and the second videostreams.

It should be noted that the phrase “update the object detection system”may mean update/train a detection algorithm of the object detectionsystem so that it through self-learning can improve object detection.

In some embodiments, the step of determining the position of the objectcomprises determining a distance and an angle to the object in relationto the first vehicle configured to obtain the first video streamcomprising video images with object detection data of the area.

An advantage with the above embodiment is that a position of thedetected object may be easily determined.

In some embodiments, the step of identifying the object in the areafurther comprises identifying an object type of the object to be one ormore of a person, a vehicle, fixed object, moving object or an animal.

An advantage with the above embodiments is that several different typesof objects may be detected, such as persons being pedestrians or bicycleriders, Segway riders, kick bike riders, children, people in electricwheel chairs or vehicles such as trucks, other cars, trailers, motorbikes, and agricultural vehicles such as tractors and combines; fixedobjects such as houses, rocks, trees, walls and signs; moving objectssuch as strollers, prams, wheel chairs, skate boards, shopping carts andlorries; or animals such as dogs, cats, horses, reindeers, wild hogs andrabbits. Of course, these are just examples, other types of persons,vehicles, fixed object, moving objects or animals are possible to bedetected by means of the embodiments disclosed herein.

In some embodiments, object type may relate to free space, such asbackground. I.e. when there is no object/object type to detect.

An advantage with detection free space is that the object detectionsystem may train itself to determine when there actually is an object todetect, and when there is no object to detect.

In some embodiments, the step of obtaining the second video streamcomprising video images with object detection data of the area comprisesidentifying vehicles recording respective video streams comprising videoimages with object detection data of the area and requesting to receivethe respective video streams.

An advantage of the above embodiment is that a larger amount of videodata covering the area may be gathered. Hence, data covering differentangles and distances of the same location may be used to train andupdate the object detection system leading to high granularity and amore reliable object detection.

In some embodiments, the second video stream comprising video imageswith object detection data of the area comprises un-validated objectdetection data at the position of the object.

An advantage of the above embodiment is that video streams from othervehicles covering the desired location and area are preferably requestedif they comprises detection data that is un-validated, i.e. it is notvalidated if the captured data actually comprise the validated object.By using un-validated object data, the training algorithm of the objectdetection system may be improved. The un-validated object data may becorrelated to the validated object data comprised in e.g. the firstvideo stream, and it may hence be determined that the un-validated datais in fact validated data which may be used for training the system. Thecorrelation may e.g. be made through pattern recognition or bydetermining a confidence value denoting a match between the data or aprobability that the data comprise the same object.

A second aspect is an object detection system comprises a control unitand validated object detection data of one or more objects. The controlunit is configured to perform the steps of:

-   -   obtaining a first video stream comprising video images with        object detection data of an area,    -   identifying an object in the area by identifying one or more        first object detection data of the object in the first video        stream corresponding to validated object detection data,    -   determining a position of the object in the area,    -   obtaining a second video stream comprising video images with        object detection data of the area,    -   identifying second object detection data of the second video        stream at the position of the object and determining whether the        second object detection data is un-validated object detection        data,    -   updating the validated object detection data of the object        detection system if it is determined that the second object        detection data of the object is un-validated object detection        data, thereby training the object detection system to recognize        patterns in un-validated object detection data.

An advantage with the above aspect is that training of an objectdetection system for a vehicle becomes more reliable in terms ofdetecting objects since video streams from different vehicles featuringthe same area are used for object detection. Hence, if one vehicle hasconfirmed that it has recorded e.g. a pedestrian, then the video streamsof other vehicles recording the same area may be used to update theobject detection system in order to provide further and different videoimages of the detected object.

Another advantage with the above embodiments is that by identifying andusing object detection data that comprise un-validated object detectiondata (i.e. it is not confirmed that the data actually portray theobject), the system may be trained to recognize the object even if thevideo image data is not complete, or the line of sight is obstructed,since the system will know that it is confirmed that an object is atthat location. Hence, a better training algorithm can be developedcompared to if only confirmed images of the object were used fortraining.

In some embodiments, the object detection system is comprised in avehicle.

In some embodiments, the object detection system is comprised in aremote server.

In some embodiments, the object detection system comprises a system withseveral units. The units may e.g. be comprised in vehicles and servers.In some embodiments, the system may be comprised only in vehicles.

In some embodiments, the control unit is configured to be connected toand receive video streams comprising video images with object detectiondata from at least a first and a second vehicle.

An advantage with the above embodiments is that data for an area may begathered easily. By gathering data from more than one vehicle over thesame area, the object detection system may receive diverse data coveringthe same area, which may be used for updating the system. The diversedata provides better training and higher granularity compared to if datawas only received from one vehicle.

In some embodiments, the control unit is configured to store, at thesecond vehicle, the second video stream comprising video images withun-validated object detection data.

An advantage with the above embodiment is that data storage may bereduced since not all video streams have to be stored, but rather videostreams that may be used as learning material (i.e. comprisingun-validated object detection data) may be stored locally and used at alater point in time.

In some embodiments, the control unit is configured to determine a timestamp of the first and second video streams and a vehicle location,wherein the time stamp indicates when the respective video stream wasobtained and wherein the vehicle location indicates a geographicallocation where the first and second video streams were obtained.

An advantage with the above embodiment is that only video streams thathave been obtained/recorded at a valid point in time may be taken intoconsideration. I.e. vehicles that have video streams covering the areaof the object, but which were recorded at a different point of time thana video stream having identified valid object detection data may not betaken into consideration.

Another advantage with the above embodiments is that the geographicallocation and/or orientation of the vehicle recording the video stream isdetermined and taken into account in order to easier determine theposition of the object and in some embodiments to determine which videostreams should be taken into account for training the system.

In some embodiments, the control unit is configured to determiningwhether the second object detection data is un-validated objectdetection data by correlating the second object detection data tovalidated object detection data and based on the correlation determininga confidence value of the second object detection data, wherein if theconfidence value is determined to be below a confidence threshold, thesecond object detection data is determined to be un-validated objectdetection data.

An advantage with the above embodiments is that determining acorrelation, e.g. a confidence value, between validated object detectiondata and object detection data of the video stream quick determinationof whether the object detection data of the video stream is valid orun-valid object detection data may be done.

In some embodiments, the control unit is configured to identifyingsecond object detection data comprising un-validated object detectiondata of the second video stream at the position of the object.

In some embodiments, the step of identifying the object in the areafurther comprises identifying an object type of the object to be one ormore of a person, a vehicle, fixed object, moving object or an animal.

An advantage with the above embodiments is that several different typesof objects may be detected, such as persons being pedestrians or bicycleriders, Segway riders, kick bike riders, children, people in drivemotors; or vehicles such as trucks, other cars, trailers, motor bikes,and agricultural vehicles such as tractors and combines; fixed objectssuch as houses, rocks, trees, walls and signs; moving objects such asstrollers, prams, wheel chairs, skate boards, shopping carts andlorries; or animals such as dogs, cats, horses, reindeers, wild hogs andrabbits. Of course, these are just examples, other types of persons,vehicles, fixed object, moving object or animals are possible to bedetected by means of the embodiments disclosed herein.

In some embodiments, the control unit is configured to identifyingvehicles recording respective video streams comprising video images withobject detection data of the area and request to receive the respectivevideo streams.

An advantage with the above embodiments is that a larger amount of videodata covering the area may be gathered. Hence, data covering differentangles and distances of the same location may be used to train andupdate the object detection system leading to high granularity and amore reliable object detection.

In some embodiments, the requested video streams comprises video imageswith object detection data of the area, the object detection datacomprising un-validated object detection data associated with theposition of the object.

An advantage with the above embodiment is that object data which isassociated with the position of the object (i.e. the video stream hascovered the position), but which has not been validated to comprisevalidated object data (i.e. an object which should be detected such as apedestrian) may be validated to comprise the object based on object dataobtained from another video stream where the object has been validated.Hence, the training algorithm is given better granularity and morereliable object detection.

A third aspect is a computer program comprising instructions, which,when the program is executed by a computer, cause the computer to carryout the method according to the first aspect.

In some embodiments, any of the above aspects may additionally havefeatures identical with or corresponding to any of the various featuresas explained above for any of the other aspects.

Further features and advantages of the invention will become apparentwhen studying the appended claims and the following description. Theskilled person in the art realizes that different features of thepresent disclosure may be combined to create embodiments other thanthose explicitly described hereinabove and below, without departing fromthe scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described in detail in the following, withreference to the attached drawings, in which

FIG. 1 shows a flow chart illustrating method steps according to someembodiments,

FIG. 2 shows schematically, an example detection scenario according tosome embodiments,

FIG. 3 shows block diagram of an example system according to someembodiments, and

FIG. 4 shows a block diagram of an example computer program according tosome embodiments.

DETAILED DESCRIPTION

Various aspects of the disclosure will hereinafter be described inconjunction with the appended drawings to illustrate and not to limitthe disclosure, wherein like designations denote like elements, andvariations of the described aspects are not restricted to thespecifically shown embodiments, but are applicable on other variationsof the disclosure.

FIG. 1 illustrates an example method 1 according to some embodiments.The method 1 is for training an object detection system 10 for a vehicle100, 31, 32, 33, 34. The object detection system 10 comprises validatedobject detection data VODD of one or more objects, i.e. object datawhere it is confirmed that the object data portrays the one or moredesired object to be detected.

The method 1 starts in step S1 with obtaining a first video stream VS1comprising video images with object detection data ODD of an area 21.Object detection data is made up of video images of the video stream VS1capturing the immediate surroundings of a vehicle. Immediatesurroundings may e.g. be a 5, 10, 50, 100, 400, 600 or more meter radiusextending from the car. In step S2 the method continues with identifyingS21 an object 22 in the area 21 in the immediate surroundings of thevehicle by identifying one or more first object detection data ODD1 ofthe object in the first video stream VS1 corresponding to validatedobject detection data VODD.

Validated object detection data may e.g. be stored in the objectdetection system and comprise a database of validated objects. Validatedobjection data may e.g. correspond to a multitude of images portrayingvarying objects in varying settings, which helps the object detectionsystem to learn and recognize (i.e. be trained for detection) and hencedetect objects captured in the video stream.

Alternatively or additionally, in some embodiments, validated objectdetection data is a detection algorithm analyzing (e.g. by applyingpattern recognition) the content of the object detection data of thevideo stream in order to determine whether the content comprise e.g.pixels forming an object that should be detected.

In order to determine whether the object detection data of the firstvideo stream is validated object detection data, the obtained firstobject detection data may be correlated against the validated objectdetection data of the object detection system. The correlation may e.g.comprise comparing the video image content of the first video stream toor with the valid object detection data and determine a correlationresult or confidence value for indicating a probability that the firstobject detection data is valid object detection data. If the correlationvalue or confidence result indicates that there is e.g. a higher than60% probability that the first object detection data comprise an videoimage of e.g. a person, the first object detection data may be labeled,or determined as valid object detection data. It should be noted that60% is just an example, and values ranging both higher and lower arecontemplated.

The method 1 then continues in step S3 with determining a position 23 ofthe object 22 in the area 21. In some embodiments, determining aposition 23 of the object 22 may also comprise tagging the position 23with a time stamp, i.e. determining a time stamp of the video stream.The time stamp may enable the object detection system 10 to collect datacollected within a predetermined time range. For example, for movingobjects such as pedestrians, cyclers, vehicles etc. there may be littlepoint in collecting video streams over the same area that are capturedhours or days later. However, in some scenarios where objects have beendetected to be at a validated location for longer periods of time (e.g.if the object is fixed, or if a pattern of movement has been detectedsuch as a commuter being detected at the same area approximately thesame time every day) it may be beneficial to collect video streamscovering the area for longer periods of time as well.

In some embodiments, determining the position 23 of the object 22 mayalternatively or additionally comprise determining a vehicle locationand/or vehicle orientation of the vehicle recording the respective videostream. By associating the video stream with the vehicle location andorientation more information may be taken into account when determiningthe location of the object, which may also assist other vehicles in thearea to pin point the location of the object. The location of thevehicle may also be of importance when determining whether or not avideo stream should be gathered for object detection system training.

In step S4, the method 1 comprises obtaining a second video stream VS2comprising video images with object detection data ODD of the area 21.As described above, object detection data is made up of video imagescapturing the immediate surroundings of the vehicle.

Hence at least two video streams covering the same area are obtained.

In some embodiments, a time stamp is determined for both the first andthe second video stream.

Then, in step S5 the method comprises identifying second objectdetection data ODD2 of the second video stream VS2 at the position 23 ofthe object 22 and determining whether the second object detection dataODD2 is un-validated object detection data UODD. Hence, the method 1 maycomprise that the second video stream is analyzed such that video imagescomprising the position of the object is taken into account.

In some embodiments, the step S51 of determining whether the secondobject detection data ODD2 is un-validated object detection data UODDmay comprise correlating the second object detection data ODD2 tovalidated object detection data VODD and based on the correlationdetermining a confidence value of the second object detection data ODD2.If the confidence value is determined to be below a confidencethreshold, the second object detection data ODD2 is determined to beun-validated object detection data UODD.

Hence, the method 1 may comprise identifying or determining that atleast one vehicle 100, 31, 32, 33, 34 (e.g. the second vehicle) is notbeing able to verify that what is seen in the object detection data ODDof the second video stream VS2, at the position 23 of the object 22, isactually the object 22. Thus, the second video stream VS2 compriseun-validated object detection data UODD associated with the position 23of the object 22.

In some embodiments, the second object detection data ODD2 may becorrelated against the validated object detection data VODD of the firstvideo stream VS1.

Hence, the validated object detection data VODD of the first stream VS1may be used in order to determine whether the second object detectiondata ODD2 is valid or un-valid object detection data. If the secondobject detection data ODD2 is determined to be un-valid object detectiondata UODD based on the correlation with the first video stream VS1, theobject detection system 10 may use the un-valid object detection dataUODD of the second stream VS2 in order to train itself to find a patternand better recognize objects. This may be enabled since the first videostream VS1 comprise validated object detection data VODD over an object22 at a determined position. The second video stream VS2 covering thesame area 21 and position 23 should hence also see and be able to verifythe object 22. However, the second video stream VS2 may for some reasoncomprise inferior quality or be partially obstructed and only parts ofthe object are discernible, but not enough to perform a correlation orpattern detection that results in valid object detection. In such case,the second video stream VS2 still possibly show the object 22, but thealgorithm of the object detection system 10 is not able to verify it.Thus, the object detection system 10 may use the un-validated objectdetection data UODD for training itself to recognize objects based onthe fact that the first video stream VS1 comprises validated objectdetection data VODD of the object 22, and the second video stream VS2should possibly hence as well.

In some embodiments, un-validated data may be determined as un-validateddata simply because the object may have moved rapidly out of the way, orwalked behind a tree or is obscured by a passing vehicle, etc. and asecond vehicle is simply not seeing it. In such scenarios, theun-validated data may still be used for training the system. It may e.g.be beneficial for the algorithm to learn how do recognize backgrounddata (free space), i.e. scenes where there is no object to detect.Hence, the object detection system may train itself to recognize bothobjects and non-objects based on un-validated object detection data(which data may comprise both un-validated objects and non-existingobjects.

When at least two video streams have been captured and the objectdetection data associated with each respective video stream has beenanalyzed/identified, the method continues in step S6 with updating thevalidated object detection data VODD of the object detection system 10if it is determined that the second object detection data ODD2 of theobject 22 is un-validated object detection data UODD. In someembodiments, the method may also comprise updating the validated objectdetection data VODD with the un-validated object detection data UODDbased on the validated object detection data VODD of the first videostream VS1.

In some embodiments, the step S1 of the method 1 comprising obtainingthe first video stream VS1 comprising video images with object detectiondata ODD of the area may optionally further comprise receiving in stepS11 the first video stream VS1 from a first vehicle.

The object detection system 10 may be located in one or more vehicles100, 31, 32, 33, 34 but may also communicate wirelessly with and/orcomprise a server 200 in e.g. a network cloud. The server 200 may e.g.collect video streams from the first vehicle 100, 31 as well as fromother vehicles 100, 31, 32, 33, 34 and perform the training of theobject detection system 10 based on the received video streams and theobject detection data ODD comprised therein. The server 200 may thenupdate the validated object detection data VODD (e.g. an objectdetection algorithm) and push this update through the network cloud tothe object detection system 10 of each respective vehicle 100, 31, 32,33, 34.

In some embodiments, step S4 of the method 1 comprising obtaining thesecond video stream VS2 comprising video images with object detectiondata ODD of the area may optionally further comprise receiving in stepS41 the second video stream VS1 from a second vehicle 100, 32, 33, 34.

Hence, in some embodiments, the server as described above may obtainvideo streams from a second vehicle. In some embodiments, the firstvehicle 100, 31 may be configured to obtain the second video VS2 streamfrom the second vehicle 100, 32, 33, 34. In some embodiments, the firstvehicle may transfer the first; the first and the second; or only thesecond video stream to the server for update of the object detectionsystem.

Likewise, in some embodiments, the second vehicle may obtain the firstvideo stream VS1 from the first vehicle and may transfer the first videostream; the second video stream; or both the first and second videostream to the server. In some embodiments, the object detection systemof the respective first and second vehicle may perform the objectdetection data update locally in each vehicle without involving anexternal server 200.

In some embodiments, the second vehicle may further store the secondvideo stream VS2 comprising un-validated object detection data UODD. Bystoring the video stream comprising video images with un-validatedobject detection data UODD locally at the second vehicle (or at thevehicle that captured the video stream comprising video images withun-validated object detection data) data storage and storage space maybe improved. The vehicles and/or server may focus on storing videostreams that comprise un-validated object detection data in favor ofstoring validated object detection data. The stored streams may then beused for training the algorithm/object detection system at a laterstage.

In some embodiments, the step of determining S3 the position of theobject in the method 1 may optionally comprise the step S31 comprisingdetermining a distance D1, D2, D3 and an angle α1, α2, α3 to the objectin relation to the first vehicle configured to obtain the first videostream VS1 comprising video images with object detection data ODD of thearea. Hence, the method 1 may enable a vehicle to determine the location23 of a detected object 22, which location 23 may then be used whengathering and analyzing other video streams of the area 21 in order todetect or not detect the object 22 in other video streams and thus trainthe object detection system to recognize un-validated object detectiondata UODD as validated object detection data VODD.

In some embodiments, the step S2 in method 1 of identifying (step S21)the object in the area may optionally further comprises the step S22 ofidentifying an object type of the object 22 to be one or more of aperson, a vehicle, fixed object, moving object or an animal.

By identifying the object type, restrictions can be made in how muchobject detection data should be gathered and for how long. If the objecttype is determined to be a person, a vehicle, moving object or an animalthen it may be of greater interest to collect video streams in a shorterperiod time than if the type of object is determined to be a fixedobject. Furthermore, for fixed objects it may be of more interest tofocus on gathering un-validated object detection data in order to trainthe object detection system to better recognize the object. For example,if the first vehicle determines that it has detected the fixed object,then its video stream comprising video images with the first objectdetection data may not have much value for training the system. If thesecond vehicle comes to the area where there is supposed to be a fixedobject that should be detected, but fails to detect it in the secondvideo stream even though the video stream is covering the area and thesupposed position of the object, then that second video streamcomprising video images with the un-validated object detection data maybe of greater interest when training the system.

In some embodiments, the step S4 of the method 1 comprising obtainingthe second video stream VS2 comprising video images with objectdetection data ODD of the area may optionally comprise the step S42 ofidentifying vehicles 100, 31, 32, 33, 34 recording respective videostreams VS comprising video images with object detection data ODD of thearea and requesting to receive the respective video streams VS.Recording, and in some embodiments requesting, may further be comprisedin optional step S7 of the method 1. In some embodiments, the recordingand requesting may comprise two different method steps, e.g. step S7 ofrecording and step S8 (not shown in FIG. 1) of requesting.

In some embodiments, the second video stream VS2 comprising video imageswith object detection data ODD of the area 21 comprises un-validatedobject detection data UODD at the position 23 of the object 22. Asdescribed above, the object detection data of the second video streammay e.g. comprise blurry video images, or partial video images whichleads to that it cannot be confirmed whether the object detection dataassociated with the position of the object actually shows the object,and the object detection data hence comprise un-validated objectdetection data at the position of the object.

The method 1 has been described as being performed in a series of methodsteps in a validated order. It should be noted that the order of thesteps may in some embodiments be another than that described above. Forexample, in some embodiments, the steps S4 and S5 may switch place withthe steps of S1 and S2. The method 1 as described above defines ascenario where a first vehicle has detected an object and the objectdetection system of the first vehicle has validated it as validatedobject detection data. I.e. the object detection system of the firstvehicle has validated that is has detected e.g. a person, vehicle, fixedobject, animal etc. and may ask other vehicles in the area whether theysee the same. If the other vehicles in the area determines that they donot, i.e. their object detection data associated with the position ofthe object and comprised in their respective videos stream isun-validated object detection data, then their video streams comprisingvideo images with the un-validated object data may be used to updatevalidated object detection data and hence train the system.

However, in some embodiments, a vehicle may obtain a video stream of anarea, the video stream comprising video images with object detectiondata. The object detection system of the vehicle may react to thatsomething is present in the object detection data, but it cannot beverified what it is. The video stream may e.g. be of inferior qualitybecause of weather conditions (rain may e.g. result in blurred orinferior video images that are hard to interpret), or the video imagesare partly obstructed, or are blurry or for any other reasons do notprovide object detection data that can be matched to validated objectdetection data. For example, when determining whether the video streamcomprise video images with validated object detection data, the systemmay determine that a confidence value of the object detection dataindicates a 42% probability that the data shows a person. Theprobability is not high enough to safely assume that the object is aperson, but high enough to determine that there might be a person.Hence, validation is needed. The vehicle (or the object detection systemcomprised in the vehicle) may then inquire with other vehicles in thearea if their video streams have captured validated object detectiondata associated with the location of the un-validated object detectiondata. The vehicle may receive the video streams from the other vehiclesthat comprise validated detection data, and may then update the objectdetection system based on the obtained/received un-validated andvalidated object detection data.

In some embodiments, the vehicle(s) may transmit their respective videostreams to an external server in order to update the object detectionsystem.

FIG. 2 illustrates an example scenario where the method and embodimentsdescribed above may be applicable.

In FIG. 2, four motor vehicles 100 are present on a road. In particulara first vehicle 31, a second vehicle 32, a third vehicle 33 and a fourthvehicle 34. The first vehicle 31 may e.g. be the first vehicle asdescribed in conjunction with FIG. 1. Likewise, the second 32, third 33and fourth 34 vehicle may be the second vehicle as described inconjunction with FIG. 1. In FIG. 2 the vehicles 31, 32, 33 and 34 areillustrated as cars, this should be seen merely as an example as othertypes of vehicles are also possible, such as trucks, motor bikes,recreational vehicles, busses, etc.

The first 31, second 32, third 33 and 34 fourth vehicle are all equippedwith a respective object detection system 10. The object detectionsystem 10 of each respective vehicle provides obtaining a respectivevideo stream VS1, VS2, VS3 and VS4 (VS4 is not shown in FIG. 2 forreasons that will be explained below), of an area 21. In the area 21, anobject 22 is present at a position 23. The object 22 is illustrated as abicyclist in FIG. 2, this is however just an example (for simplicity,all though a bicycle also is considered as a vehicle, the cyclist ofFIG. 2 is not denoted as vehicle but as the object in this disclosure).The object 22 could also be any other type of object such as apedestrian/person, vehicle, fixed object or animal, as described inconjunction with FIG. 1. The object 22 could furthermore be situated atanother location than in the middle of road, such as on the pavement orsimilar.

In some embodiments, the object detection system 10 of the vehicles maybe configured to transmit the recorded video streams VS1-VS4 to anexternal server 200 illustrated as a cloud in FIG. 2.

It should be noted that the video streams illustrated in FIG. 2 areexemplary, and may have other ranges. E.g., the video streams couldcover a semicircle spanning 180 degrees from the object detectionsystems. The range and coverage of the video streams may be dictated bythe type of unit that is recording the streams. Some cameras may e.g.record a full circle of 360, degrees, other may record a part of acircle of e.g. 270, 180, 90, 60 etc. degrees. It should also be notedthat circular ranges are an example and other shape of ranges arecontemplated. Furthermore, the length of the ranges may also varyaccording to the limitations of the recording camera/unit.

The object detection system 10 of the first vehicle 31 may thus obtainthe first video stream VS1 comprising video images with object detectiondata of the area 21. The object detection system 10 of the first vehicle31 may then identify the object 22 in the area 21 by identifying one ormore first object detection data ODD1 in the first video stream VS1corresponding to validated object detection data VODD.

For example, the one or more first object detection data ODD1 may e.g.clearly show the person on the bicycle (i.e. the cyclist) in the road.The object detection system 10 of the first vehicle 31 may comprise adatabase of validated object detection data, and when comparing the oneor more first object detection data ODD1 to the validated objectdetection data there is a clear match and the object detection system ofthe first vehicle may then determine that it sees/has detected an object22. In some embodiments, the validated object detection data of theobject detection system may alternatively or additionally be analgorithm instructing the object detection system what to look for inobject detection data in order to determine whether the object detectiondata is valid or not. The algorithm may e.g. comprise a series ofpatterns that should be fulfilled when analyzing the pixels of the videostreams in order to determine valid or un-valid object detection data.

Then, the object detection system 10 of the first vehicle may determinethe position 23 of the object 22 in the area 21. The first detectionsystem may e.g. determine a distance D1 and an angle α1 to the object 22in relation to the first vehicle 31. In some embodiments, the objectdetection system 10 may further tag the first video stream VS1 of thearea 21 and the object 22 with a time stamp.

In some embodiments, the object detection system 10 may be configured todetermining a time stamp of the first and second video streams, the timestamp indicating when the respective video stream was obtained.

In some embodiments, the object detection system 10 may be configured todetermining a vehicle location and/or orientation of the vehiclerecording the respective video stream. The video stream may be tagged(in addition or alternatively to the time stamp) with the vehiclelocation. The vehicle location may be a geographical location denotingthe physical position of the vehicle and may be determined by means ofe.g. GPS.

When the object detection system 10 of the first vehicle has determinedthat it sees an object, it may inquire other vehicles in the area, e.g.the second 32, third 33 and fourth 34 vehicles if they have a videostream over the area, and if they see the object 22. The objectdetection system of the first vehicle 31 may e.g. identify othervehicles recording video streams comprising video images with objectdetection data of the area. In some embodiments, the second 32 and third33 vehicle may respond whereas the fourth vehicle 34 may not since itdoes not capture the area 21 (and its video stream VS4 is hence notshown in FIG. 2). Whether or not the fourth vehicle should respond maybe dictated by the time stamp. If e.g. only real time video streams areof interest, the video stream of the fourth vehicle is not of interestsince it does not capture the area 21 at the required time.

In some embodiments, the second 32 and third 33 vehicles may respondonly if they determine the object detection data of each respectivevideo stream VS2 and VS3 to comprise un-validated object detection dataUODD associated with the position 23 of the alleged object 22 in thearea 21. The second and third vehicles may try to identify the object 22by e.g. also determine a distance D2, D3 and angle α2, α3 to the allegedobject 22 in relation to the second and third vehicles respectively, anddetermine if the object detection data associated with the position isun-validated UODD or validated object detection data VODD. If the secondand/or third object detection data ODD2, ODD3 of the second and/or thirdvideo streams VS2, VS3 is determined to be un-validated object detectiondata UODD, the second and/or third video streams comprising video imageswith un-validated object detection data UODD may be used for updatingand thereby training the object detection system. In some embodiments,the object detection system 10 of the first vehicle may obtain all videostreams of the area 21 (in FIG. 2, the VS2 and VS3) and identify whetherthe comprised second and third object detection data ODD2, ODD3 of VS2and VS3 comprise the object 22 by trying to identify the object 22 atthe position 23 in the area 21 in the video streams VS2 and VS3. Theobject detection system 10 of the first vehicle may further updatevalidated object detection data with the object detection data obtainedfrom the second and/or third video stream.

In some embodiments, the video streams VS1, VS2 and VS3 may be obtainedby the external server 200 (e.g. a server in the cloud, as shown in FIG.2) over a network connection such as the internet. The external servermay perform the update of the validated object detection data if it isdetermined that obtained un-validated object detection data UODDpossibly is correlated with validated object detection data VODD of thearea, e.g. from the first video stream, (and thus train e.g. thealgorithm of the object detection system that performs the detection)and then push the update to the object detection systems 10 of thefirst, second, third and fourth vehicles so that the respective objectdetection system 10 may be trained to recognize objects in varioussettings.

Above, it has been described that the object detection system is updatedbased on a first vehicle detecting validated object detection data (i.e.the first vehicle knows what object it is seeing) and other un-validatedobject detection data is then gathered from vehicles that cannot verifythat they see the object in the same position. The gathered data is thenused to update/train the system. E.g., a blurry video image, video imagewith inferior resolution or partial video image of the object (i.e.un-validated object detection data) may be correlated to the validatedobject detection data of the object. For example, the first vehicle maydetect a pedestrian at a distance of 20 m. Detection is certain and isbased on a video image of the pedestrian having a size of e.g. 200*50pixels. The second vehicle may be further away and sees the same areabut at a 250 m distance. The pedestrian may in such case be captured by10*3 pixels which gives less good resolution than what the first vehiclecould obtain and it may hence be more difficult for the second vehicleto know what it's seeing and the video stream of the second vehicle ishence valuable for training the system.

The video images may e.g. compared to each other and details matched toconfirm that the un-validated object detection data from the secondvehicle is in fact validated object detection data and determine e.g. apattern in the un-validated object detection data that may be used inthe future to determine whether object detection data is validated orun-validated. The next time a similarly blurry or incomplete video imageof an object is captured in a video stream, the updated/trained objectdetection system may determine that the video image is validated objectdetection data based on the updated training algorithm of the objectdetection system.

It should be noted that the order of the vehicles are exemplary. Thefirst vehicle may e.g. be the second, third or fourth vehicle and viceversa.

Furthermore, in some embodiments, the method may start with a vehicledetermining that it cannot validate that what it is actually seeing inits video stream of an area is a validated object. E.g., the thirdvehicle 33 may detect un-validated object detection data UODD in itsvideo stream VS3 at the position 23 of the area 21. The video stream VS3may e.g. comprise a partially obstructed video image of the object 22.In FIG. 2, the line of sight to the object 22 from the third vehicle ise.g. partially obstructed by the second vehicle 32. If a correlation isperformed, a confidence value of the object detection data of the videostream may e.g. be 40% which may not be high enough to pass a thresholdfor validated object detection data, but still be high enough such thatthe object detection system determines that there might be an object inthe video stream that should be detected.

The third vehicle 33 may then after having determined that the thirdvideo stream VS3 comprise un-validated object detection data UODDassociated with/at the position 23 of the area 21, identify othervehicles that have recorded a respective video stream covering the area21. The third vehicle 33 may e.g. send out an enquiry to other presentvehicles if they have detected validated object detection dataassociated with the position. In some embodiments, the second 32 andfirst 31 vehicle may respond by transmitting their respective videostream comprising video images with validated object detection data ofthe object 22 to the third vehicle 33. The third vehicle may then updatethe object detection system based on the determined un-validated objectdetection data and possibly the validated object detection data.

In some embodiments, the third vehicle 33 may locally update its objectdetection system 10, and possibly transmit the update to the othervehicles such that their respective object detection systems 10 areupdated as well. In some embodiments, the third vehicle may transfer theobtained un-validated and possibly the validated object detection datato an external server comprising an object detection system and adatabase of validated object detection data and/or algorithms forrecognizing validated object detection data. The external server maythen use the obtained data to train/update the object detection systemand possibly push the update to all object detection systems connectedto the server and associated with a vehicle (e.g. the respective objectdetection system 10 associated with vehicles 31, 32, 33 and 34).

FIG. 3 illustrates in a block diagram an object detection system 10 fora vehicle 100 according to some embodiments. The object detection system10 may e.g. be the object detection system as described in conjunctionwith any of the previous FIGS. 1-2. The vehicle 100 may e.g. be any ofthe vehicles as described in conjunction with FIGS. 1-2.

The object detection system 10 according to FIG. 3 comprises a controlunit 11 (CNTR) and an object detection data module 112 (ODD) comprisingvalidated object detection data 113 (VODD) and un-validated objectdetection data 114 (UODD) of one or more objects.

In some embodiments, the control unit 11 may comprise controllingcircuitry. The control unit/controlling circuitry may comprise theobject detection data module 112 for storing object detectiondata/algorithms of validated objection data 113 and un-validated objectdetection data 114. In some embodiments, the control unit may furthercomprise a video unit 111 (VID), and a determining unit 115 (DET). Insome embodiments, the object detection system 10 may further comprise anantenna circuit 12 (RX/TX).

The control unit 11 is configured to perform obtaining of a first videostream (e.g. the VS1 of FIG. 2) comprising video images with objectdetection data of an area (compare with step S1 of the method 1). Thecontrol unit 11 may e.g. be configured to cause the video unit 111 torecord and relay a first video stream VS1 and cause the object detectiondata module 112 to store the object detection data ODD of the firstvideo stream VS1.

The control unit may further be configured to cause identifying of anobject 22 in the area 21 by identifying one or more first objectdetection data ODD1 of the object 22 in the first video stream VS1corresponding to validated object detection data VODD (compare with stepS2 of the method 1). The control unit 11 may e.g. be configured to causethe ODD module 112 to determine whether the obtained object detectiondata corresponds to validated objection data or un-validated objectdetection data e.g. by using an algorithm for object detection andstoring the object detection data as either validated object detectiondata 113 or un-validated object detection data 114.

The control unit 11 may be configured to cause determining of a position23 in the area 21 of the object 22 (compare with step S3 in the method1). The control unit 11 may e.g. be configured to cause the determiningmodule 115 to determine the position 23.

The control unit 11 may be configured to cause obtaining of a secondvideo stream VS2 comprising video images with object detection data ofthe area (compare to step S4 in method 1). The control unit 11 may e.g.be configured to cause the antenna circuit 12 to receive the secondvideo stream VS2.

The control unit 11 may be configured to cause identifying of secondobject detection data ODD2 of the second video stream VS2 at theposition 23 of the object 22, (compare with step S5 of method 1) anddetermine whether the second object detection data ODD2 is un-validatedobject detection data UODD. The control unit 11 may e.g. cause theobject data detection module 112 to analyze the second object detectiondata ODD2 by means of stored algorithms for validated object detectiondata VODD, and/or match second object detection data ODD2 to storedvalidated object detection data VODD or to the validated objectdetection data VODD of the first video stream VS1. The control unit 11may e.g. be configured to cause the object detection module 112 todetermine whether the second object detection data ODD2 matches tovalidated detection data VODD stored in the module 112, 113. When nomatch is determined, the second object detection data ODD2 may be seenand identified as un-validated detection data UODD at the position 23 ofthe object 22.

In some embodiments, the control unit 11 may be configured to causedetermining whether the second object detection data ODD2 isun-validated object detection data VODD by correlating the second objectdetection data ODD2 to validated object detection data VODD and based onthe correlation determining a confidence value of the second objectdetection data, wherein if the confidence value is determined to bebelow a confidence threshold, the second object detection data isdetermined to be un-validated object detection data (compare with stepS51 of method 1).

The control unit 11 may be configured to cause updating of the validatedobject detection data 113 of the object detection system 10 if it isdetermined that the second object detection data of the object 22 isun-validated object detection data UODD. The control unit 11 may e.g.cause the object detection module 112 to store the second objectiondetection data in the validated object detection database 113 asvalidated objection detection data and/or update a stored detectionalgorithm.

In some embodiments, the control unit 11 is configured to be connectedto and receive video streams comprising video images with objectdetection data from at least a first and a second vehicle 100, 31, 32,33, 34 (compare with method 1 and FIG. 2). The control unit 11 may e.g.be configured to cause the video module 111 to record a video stream,and/or cause the antenna circuit 12 to receive one or more video streamsfrom at least a first and a second vehicle.

In some embodiments, the control unit 11 is further configured to storeat the second vehicle 100, 32, 33, 34 the second video stream VS2comprising un-validated object detection data UODD.

In some embodiments, the control unit 11 is further configured todetermine a time stamp of the first and second video streams VS1, VS2,the time stamp indicating when the respective video stream was obtained.

In some embodiments, the control unit 11 is configured to identifying anobject type of the object to be one or more of a person, a vehicle,fixed object or an animal (compare with step S22 of method 1). Thecontrol unit 11 may e.g. cause the ODD module 112, possibly incooperation with the determining module 115 to determine and therebyidentify (based on the object detection data) an object type of theobject.

In some embodiments, the control unit 11 is configured to identifyingvehicles recording video streams comprising video images with objectdetection data of the area (compare with steps S42 and S7 in method 1).The control unit 11 may for example be configured to cause antennacircuit 12 to search for and identify other vehicles in the area.

In some embodiments, the control unit 11 is configured to identifyingvehicles recording video streams comprising video images with objectdetection data ODD of the area 21, the object detection data ODDcomprising un-validated object detection data UODD at the position 23 ofthe object 22 (compare with steps S42 and S7 of method 1). The controlunit 11 may e.g. be configured to cause the object detection module 112to determine that the object detection data ODD is un-validated objectdetection data UODD.

In some embodiments, the object detection system 10 as described in FIG.3 may be comprised in an external server 200. When comprised in anexternal server, the object detection system 10 may be configured tocommunicate with other object detection systems 10 comprised in vehicles100, 31, 32, 33, 34 and (wirelessly) connected to the external server200.

FIG. 4 illustrates a computer program comprising instructions, which,when the program is executed by a computer, cause the computer to carryout the methods as described in conjunction with any of the previousFIGS. 1-3.

More particularly FIG. 4 illustrates in some embodiments a computerprogram product on a non-transitory computer readable medium 400. FIG. 4illustrates an example non-transitory computer readable medium 400 inthe form of a compact disc (CD) ROM 400. The non-transitory computerreadable medium has stored thereon a computer program comprising programinstructions. The computer program is loadable into a data processor(PROC; e.g., data processing circuitry or a data processing unit) 420,which may, for example, be comprised in a control unit 410. When loadedinto the data processor, the computer program may be stored in a memory(MEM) 430 associated with or comprised in the data processor. Accordingto some embodiments, the computer program may, when loaded into and runby the data processor, cause execution of method steps according to, forexample, any of the methods illustrated in FIGS. 1-3, or otherwisedescribed herein.

Those skilled in the art will appreciate that the steps, services andfunctions explained herein may be implemented using individual hardwarecircuitry, using software functioning in conjunction with a programmedmicroprocessor or general purpose computer, using one or moreApplication Specific Integrated Circuits (ASICs) and/or using one ormore Digital Signal Processors (DSPs). It will also be appreciated thatwhen the present disclosure is described in terms of a method, it mayalso be embodied in one or more processors and one or more memoriescoupled to the one or more processors, wherein the one or more memoriesstore one or more programs that perform the steps, services andfunctions disclosed herein when executed by the one or more processors.

The present disclosure has been presented above with reference tospecific embodiments. However, other embodiments than the abovedescribed are possible and within the scope of the disclosure. Differentmethod steps than those described above, performing the method byhardware or software, may be provided within the scope of thedisclosure. Thus, according to an exemplary embodiment, there isprovided a non-transitory computer-readable storage medium storing oneor more programs configured to be executed by one or more processors ofa system for object detection, the one or more programs comprisinginstructions for performing the method according to any one of theabove-discussed embodiments. Alternatively, according to anotherexemplary embodiment a cloud computing system can be configured toperform any of the method aspects presented herein. The cloud computingsystem may comprise distributed cloud computing resources that jointlyperform the method aspects presented herein under control of one or morecomputer program products. Moreover, the processor may be connected toone or more communication interfaces and/or sensor interfaces forreceiving and/transmitting data with external entities such as e.g.sensors arranged on the vehicle surface, an off-site server, or acloud-based server.

The processor(s) (associated with the object detection system) may be orinclude any number of hardware components for conducting data or signalprocessing or for executing computer code stored in memory. The systemmay have an associated memory, and the memory may be one or more devicesfor storing data and/or computer code for completing or facilitating thevarious methods described in the present description. The memory mayinclude volatile memory or non-volatile memory. The memory may includedatabase components, object code components, script components, or anyother type of information structure for supporting the variousactivities of the present description. According to an exemplaryembodiment, any distributed or local memory device may be utilized withthe systems and methods of this description. According to an exemplaryembodiment the memory is communicably connected to the processor (e.g.,via a circuit or any other wired, wireless, or network connection) andincludes computer code for executing one or more processes describedherein.

It will be appreciated that the above description is merely exemplary innature and is not intended to limit the present disclosure, itsapplication or uses. While specific examples have been described in thespecification and illustrated in the drawings, it will be understood bythose of ordinary skill in the art that various changes may be made andequivalents may be substituted for elements thereof without departingfrom the scope of the present disclosure as defined in the claims.Furthermore, modifications may be made to adapt a particular situationor material to the teachings of the present disclosure without departingfrom the essential scope thereof. Therefore, it is intended that thepresent disclosure not be limited to the particular examples illustratedby the drawings and described in the specification as the best modepresently contemplated for carrying out the teachings of the presentdisclosure, but that the scope of the present disclosure will includeany embodiments falling within the foregoing description and theappended claims. Reference signs mentioned in the claims should not beseen as limiting the extent of the matter protected by the claims, andtheir sole function is to make claims easier to understand.

REFERENCE SIGNS

-   1: method-   100: Vehicle-   31: 1st vehicle-   32: 2nd vehicle-   33: 3rd vehicle-   34: 4th vehicle-   10: Object Detection System-   200: External server-   VS1: First video stream-   VS2: Second video stream-   VS3: Third video stream-   VS4: Fourth video stream-   ODD1: First object detection data-   ODD2: Second object detection data-   ODD3: Third object detection data-   VODD: Validated object detection data-   UODD: Un-validated object detection data-   D1-3: Distance to object from vehicle-   α1-3: Angle to object relative vehicle-   21: Area-   22: Object-   23: Position-   11: Control unit, CNTR-   12: Antenna circuit, RX/TX-   111: Video unit, VID-   112: Object detection data module, ODD-   113: Validated object detection data, VODD-   114: Un-validated object detection data, UODD-   115: Determining module, DET-   400: Computer program product-   410: Data processing unit-   420: Processor-   430: Memory

What is claimed is:
 1. A method for training an object detection systemfor a vehicle, wherein the object detection system comprises validatedobject detection data of one or more objects, the method comprising thesteps of: obtaining a first video stream comprising video images withobject detection data of an area, identifying an object in the area byidentifying one or more first object detection data of the object in thefirst video stream corresponding to validated object detection data,determining a position of the object in the area, obtaining a secondvideo stream comprising video images with object detection data of thearea, identifying second object detection data of the second videostream at the position of the object and determining whether the secondobject detection data is un-validated object detection data, updatingthe validated object detection data of the object detection system if itis determined that the second object detection data of the object isun-validated object detection data, thereby training the objectdetection system to recognize patterns in un-validated object detectiondata.
 2. The method according to claim 1, wherein the step of obtainingthe first video stream comprising video images with object detectiondata of the area comprises receiving the first video stream from a firstvehicle.
 3. The method according to claim 1, wherein the step ofobtaining the second video stream comprising video images with objectdetection data of the area comprises receiving the second video streamfrom a second vehicle.
 4. The method according to claim 3, furthercomprising storing, at the second vehicle the second video streamcomprising un-validated object detection data.
 5. The method accordingto claim 1, further comprising determining a time stamp of the first andsecond video streams and a vehicle location, wherein the time stampindicates when the respective video stream was obtained and wherein thevehicle location indicates a geographical location where the first andsecond video streams were obtained.
 6. The method according to claim 1,wherein determining whether the second object detection data isun-validated object detection data comprises correlating the secondobject detection data to validated object detection data and based onthe correlation determining a confidence value of the second objectdetection data, wherein if the confidence value is determined to bebelow a confidence threshold, the second object detection data isdetermined to be un-validated object detection data.
 7. The methodaccording to claim 1, wherein the step of determining the position ofthe object comprises determining a distance and an angle to the objectin relation to the first vehicle configured to obtain the first videostream comprising video images with object detection data of the area.8. The method according to claim 1, wherein the step of identifying theobject in the area further comprises identifying an object type of theobject to be one or more of a person, a vehicle, fixed object, movingobject or an animal.
 9. The method according to claim 1, wherein thestep of obtaining the second video stream comprising video images withobject detection data of the area comprises identifying vehiclesrecording respective video streams comprising video images with objectdetection data of the area and requesting to receive the respectivevideo streams.
 10. The method according to claim 9, wherein the secondvideo stream comprising video images with object detection data of thearea, comprises un-validated object detection data associated with theposition of the object.
 11. An object detection system for a vehicle,wherein the object detection system comprises a control unit andvalidated object detection data of one or more objects, the control unitis configured to perform the steps of: obtaining a first video streamcomprising video images with object detection data of an area,identifying an object in the area by identifying one or more firstobject detection data of the object in the first video streamcorresponding to validated object detection data, determining a positionof the object in the area, obtaining a second video stream comprisingvideo images with object detection data of the area, identifying secondobject detection data of the second video stream at the position of theobject and determining whether the second object detection data isun-validated object detection data, updating the validated objectdetection data of the object detection system if it is determined thatthe second object detection data of the object is un-validated objectdetection data, thereby training the object detection system torecognize patterns in un-validated object detection data.
 12. The objectdetection system according to claim 11, wherein the control unit isconfigured to be connected to and receive video streams comprising videoimages with object detection data from at least a first and a secondvehicle.
 13. The object detection system according to claim 12, whereinthe control unit is configured to store, at the second vehicle, thesecond video stream comprising un-validated object detection data. 14.The object detection system according to claim 11, wherein the controlunit is further configured to determining a time stamp of the first andsecond video streams and a vehicle location, wherein the time stampindicates when the respective video stream was obtained and wherein thevehicle location indicated a geographical location where the first andsecond video streams were obtained.
 15. The object detection systemaccording to claim 11, wherein the control unit is configured todetermine whether the second object detection data is un-validatedobject detection data by correlating the second object detection data tovalidated object detection data and based on the correlation determininga confidence value of the second object detection data, wherein if theconfidence value is determined to be below a confidence threshold, thesecond object detection data is determined to be un-validated objectdetection data.
 16. The object detection system according to claim 11,wherein the control unit is configured to identifying an object type ofthe object to be one or more of a person, a vehicle, fixed object,moving object or an animal.
 17. The object detection system according toclaim 11, wherein the control unit is configured to identifying vehiclesrecording video streams comprising video images with object detectiondata of the area.
 18. The object detection system according to claim 11,wherein the control unit is configured to identifying vehicles recordingvideo streams comprising video images with object detection data of thearea, the object detection data comprising un-validated object detectiondata at the position of the object.
 19. Anon-transitory computerreadable medium storing a computer program comprising instructionswhich, when the program is executed by a computer, cause the computer tocarry out the method of claim 1.